Module 11 Animated and Interactive Graphics
Interactive and animated graphics are one of the major advantages of using the Rmarkdown ecosystem - because you can easily create web pages in markdown (without the pain of HTML), you aren’t limited by paper any more. We’ll cover two different technologies that allow you to create different types of interactive charts, graphs, and interfaces.
It is helpful to think about interactivity in a couple of different ways:
What does it require? Do you need to be doing statistical calculations in the background, or can you precompute all of the data ahead of time?
What type of activity or interactivity do you need?
- Zoom in/out?
- Provide additional information in response to user actions (mouseover, click)
- Provide information over time (animation)
- Keep track of a data point over multiple plots? (linked plots)
- Keep track of one or more data points and change their appearance based on user interaction (brushing)
- Allow the user to change the underlying statistical model or data?
(This is not a full list of all of the types of interactivity, just a few of the more common options)
In this section, we’ll cover two ways to easily create interactive graphics or applets in R. There are, of course, many others – many javascript libraries have R extensions of one form or another.
Module Objectives
- Create interactive charts with plotly
- Use Shiny to create interactive web applets
11.1 Plotly
Plotly is a graphing library that uses javascript to add interactivity to graphics. There are several different ways to create plotly graphs in R, but by far the easiest is ggplotly, which converts a ggplot to a plotly plot automatically (so you don’t have to specify most of the details).
11.1.1 ggplotly: ggplot2 to plotly conversions
Set up the data
if (!"plotly" %in% installed.packages()) install.packages("plotly")
if (!"tidytuesdayR" %in% installed.packages()) {
devtools::install_github("thebioengineer/tidytuesdayR")
}
library(lubridate) # dates and times
library(plotly)
library(tidytuesdayR) # get interesting data
library(tidyverse)
# Load the data from TidyTuesday on May 12
full_data <- tt_load('2020-05-12')
volcano <- full_data$volcano
eruptions <- full_data$eruptions
events <- full_data$events
sulfur <- full_data$sulfur
trees <- full_data$tree_ringsLet’s try out plotly while doing a bit of exploratory data analysis on this dataset.
Cleaning up
volcano
volcano <- volcano %>%
filter(tectonic_settings != "Unknown") %>%
separate(tectonic_settings, into = c("zone", "crust"), sep = "/", remove = F) %>%
# Remove anything past the first punctuation character - that will catch (xx) and ?
mutate(volcano_type = str_remove(primary_volcano_type, "[[:punct:]].*$"))Let’s start by seeing whether the elevation of a volcano changes based on the type of zone it’s on - we might expect that Rift zone volcanos (where plates are pulling away from each other) might not be as high.
Does volcano type makes a difference?
p <- volcano %>%
ggplot(aes(x = elevation, color = volcano_type)) +
geom_density() +
# Rug plots show each observation as a tick just below the x axis
geom_rug()
ggplotly(p)Here, the interactivity actually helps a bit: we don’t need to use the legend to see what each curve corresponds to. We can see that submarine volcanoes are typically much lower in elevation (ok, duh), but also that subglacial volcanoes are found in a very limited range. If we double-click on a legend entry, we can get rid of all other curves and examine each curve one by one.
I added the rug layer after the initial bout because I was curious how much data each of these curves were based on. If we want only curves with n > 10 observations, we can do that:
p <- volcano %>%
group_by(volcano_type) %>% mutate(n = n()) %>%
filter(n > 10) %>%
ggplot(aes(x = elevation, color = volcano_type)) +
geom_density() +
# Rug plots show each observation as a tick just below the x axis
geom_rug(aes(text = paste0(volcano_name, ", ", country)))## Warning: Ignoring unknown aesthetics: textIf we want to specify additional information that should show up in the tooltip, we can do that as well by adding the text aesthetic even though geom_rug doesn’t take a text aesthetic. You may notice that ggplot2 complains about the unknown aesthetic I’ve added to geom_rug: That allows us to mouse over each data point in the rug plot and see what volcano it belongs to. So we can tell from the rug plot that the tallest volcano is Ojas de Salvado, in Chile/Argentina (I believe that translates to Eyes of Salvation?).
ggplotly makes it very easy to generate plots that have a ggplot2 equivalent; you can customize these plots further using plotly functions that we’ll see in the next section. But first, try the interface out on your own.
Try it out
Conduct an exploratory data analysis of the eruptions dataset. What do you find?
My solution
## # A tibble: 6 x 15
## volcano_number volcano_name eruption_number eruption_catego… area_of_activity
## <dbl> <chr> <dbl> <chr> <chr>
## 1 266030 Soputan 22354 Confirmed Erupt… <NA>
## 2 343100 San Miguel 22355 Confirmed Erupt… <NA>
## 3 233020 Fournaise, … 22343 Confirmed Erupt… <NA>
## 4 345020 Rincon de l… 22346 Confirmed Erupt… <NA>
## 5 353010 Fernandina 22347 Confirmed Erupt… <NA>
## 6 273070 Taal 22344 Confirmed Erupt… <NA>
## # … with 10 more variables: vei <dbl>, start_year <dbl>, start_month <dbl>,
## # start_day <dbl>, evidence_method_dating <chr>, end_year <dbl>,
## # end_month <dbl>, end_day <dbl>, latitude <dbl>, longitude <dbl>## volcano_number volcano_name eruption_number
## Min. :210010 Length:11178 Min. :10001
## 1st Qu.:263310 Class :character 1st Qu.:12817
## Median :290050 Mode :character Median :15650
## Mean :300284 Mean :15667
## 3rd Qu.:343030 3rd Qu.:18464
## Max. :600000 Max. :22355
##
## eruption_category area_of_activity vei
## Confirmed Eruption :9900 Length:11178 Min. :0.000
## Discredited Eruption: 166 Class :character 1st Qu.:1.000
## Uncertain Eruption :1112 Mode :character Median :2.000
## Mean :1.948
## 3rd Qu.:2.000
## Max. :7.000
## NA's :2906
## start_year start_month start_day evidence_method_dating
## Min. :-11345.0 Min. : 0.000 Min. : 0.000 Length:11178
## 1st Qu.: 680.0 1st Qu.: 0.000 1st Qu.: 0.000 Class :character
## Median : 1847.0 Median : 1.000 Median : 0.000 Mode :character
## Mean : 622.8 Mean : 3.451 Mean : 7.015
## 3rd Qu.: 1950.0 3rd Qu.: 7.000 3rd Qu.:15.000
## Max. : 2020.0 Max. :12.000 Max. :31.000
## NA's :1 NA's :193 NA's :196
## end_year end_month end_day latitude
## Min. :-475 Min. : 0.000 Min. : 0.00 Min. :-77.530
## 1st Qu.:1895 1st Qu.: 3.000 1st Qu.: 4.00 1st Qu.: -6.102
## Median :1957 Median : 6.000 Median :15.00 Median : 17.600
## Mean :1917 Mean : 6.221 Mean :13.32 Mean : 16.866
## 3rd Qu.:1992 3rd Qu.: 9.000 3rd Qu.:21.00 3rd Qu.: 40.821
## Max. :2020 Max. :12.000 Max. :31.00 Max. : 85.608
## NA's :6846 NA's :6849 NA's :6852
## longitude
## Min. :-179.97
## 1st Qu.: -77.66
## Median : 55.71
## Mean : 31.57
## 3rd Qu.: 139.39
## Max. : 179.58
## # Historical (very historical) dates are a bit of a pain to work with, so I
# wrote a helper function which takes year, month, and day arguments and formats
# them properly
fix_date <- function(yyyy, mm, dd) {
# First, negative years (BCE) are a bit of a problem.
neg <- yyyy < 0
subtract_years <- pmax(-yyyy, 0) # Years to subtract off later
# for now, set to 0
year_fixed <- pmax(yyyy, 0) # this will set anything negative to 0
# sometimes the day or month isn't known, so just use 1 for both.
# recorded value may be NA or 0.
day_fixed <- ifelse(is.na(dd), 1, pmax(dd, 1))
month_fixed <- ifelse(is.na(mm), 1, pmax(mm, 1))
# Need to format things precisely, so use sprintf
# %0xd ensures that you have at least x digits, padding the left side with 0s
# lubridate doesn't love having 3-digit years.
date_str <- sprintf("%04d/%02d/%02d", year_fixed, month_fixed, day_fixed)
# Then we can convert the dates and subtract off the years for pre-CE dates
date <- ymd(date_str) - years(subtract_years)
}
erupt <- eruptions %>%
# Don't work with discredited eruptions
filter(eruption_category == "Confirmed Eruption") %>%
# Create start and end dates
mutate(
start_date = fix_date(start_year, start_month, start_day),
end_date = fix_date(end_year, end_month, end_day),
# To get duration, we have to start with a time interval,
# convert to duration, then convert to a numeric value
duration = interval(start = start_date, end = end_date) %>%
as.duration() %>%
as.numeric("days"))## Warning: 1 failed to parse.## Warning: 5895 failed to parse.Let’s start out seeing what month most eruptions occur in…
# Note, I'm using the original month, so 0 = unknown
p <- ggplot(erupt, aes(x = factor(start_month))) + geom_bar()
ggplotly(p)Another numerical variable is VEI, volcano explosivity index. A VEI of 0 is non-explosive, a VEI of 4 is about what Mt. St. Helens hit in 1980, and a VEI of 5 is equivalent to the Krakatau explosion in 1883. A VEI of 8 would correspond to a major Yellowstone caldera eruption (which hasn’t happened for 600,000 years). Basically, VEI increase of 1 is an order of magnitude change in the amount of material the eruption released.
## Warning: Removed 2270 rows containing non-finite values (stat_count).We can also look at the frequency of eruptions over time. We’ll expect some historical bias - we don’t have exact dates for some of these eruptions, and if no one was around to write the eruption down (or the records were destroyed) there’s not going to be a date listed here.
p <- erupt %>%
filter(!is.na(end_date)) %>%
filter(start_year > 0) %>%
ggplot(aes(x = start_date, xend = start_date,
y = 0, yend = duration,
color = evidence_method_dating)) +
geom_segment() +
geom_point(size = .5, aes(text = volcano_name)) +
xlab("Eruption Start") +
ylab("Eruption Duration (days)") +
facet_wrap(~vei, scales = "free_y")## Warning: Ignoring unknown aesthetics: text## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.p <- erupt %>%
filter(!is.na(end_date)) %>%
# Account for recency bias (sort of)
filter(start_year > 1800) %>%
ggplot(aes(x = factor(vei), y = duration)) +
geom_violin() +
xlab("VEI") +
ylab("Eruption Duration (days)") +
scale_y_sqrt()
ggplotly(p)## Warning in self$trans$transform(x): NaNs produced## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 3 rows containing non-finite values (stat_ydensity).11.1.2 plot_ly: Like base plotting, but interactive!
You can also create plotly charts that aren’t limited by what you can do in ggplot2, using the plot_ly function.
We can start with a scatterplot of volcanoes along the Earth’s surface:
## No scattergeo mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.plot_ly(type = "scattergeo", lon = volcano$longitude, lat = volcano$latitude,
mode = "markers",
# Add information to mouseover
text = ~paste(volcano$volcano_name, "\n",
"Last Erupted: ", volcano$last_eruption_year),
# Change the markers because why not?
marker = list(color = "#d00000", opacity = 0.25)
)The
plot_ly function is also pipe friendly. Variable mappings are preceded with ~ to indicate that the visual appearance changes with the value of the variable.
# Load RColorBrewer for palettes
library(RColorBrewer)
volcano %>%
group_by(volcano_type) %>% mutate(n = n()) %>%
filter(n > 15) %>%
plot_ly(type = "scattergeo", lon = ~longitude, lat = ~latitude,
mode = "markers",
# Add information to mouseover
text = ~paste(volcano_name, "\n",
"Last Erupted: ", last_eruption_year),
color = ~ volcano_type,
# Specify a palette
colors = brewer.pal(length(unique(.$volcano_type)), "Paired"),
# Change the markers because why not?
marker = list(opacity = 0.5)
)The plotly documentation often uses plyr – which is an older version of dplyr. If you load plyr, it will seriously mess up your day – a lot of the function names are the same. So, instead, here’s a shortcut: spread is pivot_wider and gather is pivot_longer. That should at least help with understanding what the code is doing.
If you do accidentally load plyr, that’s fine: just restart your R session so that your loaded packages are cleared and you can start over. Or, if you must, you can reference a plyr function using plyr::spread without loading the package – that’s a safe way to use the plotly demo code as-is.
Let’s explore traces a bit.
According to the plotly documentation,
A trace is just the name we give a collection of data and the specifications of which we want that data plotted. Notice that a trace will also be an object itself, and these will be named according to how you want the data displayed on the plotting surface
In ggplot2 terms, it seems that a trace is somewhat akin to a geom.
trace0 <- rnorm(100, mean = 5)
trace1 <- rnorm(100, mean = 0)
trace2 <- rnorm(100, mean = -5)
data <- tibble(x = 1:100, trace0, trace1, trace2)
# Let's see how this goes with one trace
plot_ly(data, x = ~x) %>%
add_trace(y = ~trace0, name = 'trace0', mode = 'lines')## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plot.ly/r/reference/#scatter# Adding some more traces
plot_ly(data, x = ~x) %>%
add_trace(y = ~trace0, name = 'trace0', mode = 'lines') %>%
add_trace(y = ~trace1, name = "trace1", mode = 'lines+markers') %>%
add_trace(y = ~trace2, name = "trace2", mode = 'markers')## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plot.ly/r/reference/#scatter
## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plot.ly/r/reference/#scatter
## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plot.ly/r/reference/#scatterBut, if you want all of the variables to be shown with the same trace type, it’s probably easier to get to long form:
data %>%
pivot_longer(matches("trace"), names_to = "trace", names_prefix = "trace", values_to = "y") %>%
plot_ly(x = ~x, y = ~y, color = ~trace, mode = "lines+markers")## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plot.ly/r/reference/#scatterThere are many different trace types in plotly, but your best bet is to check the documentation to see what is available.
11.1.3 Animation
Plotly can also animate your plots for you.
library(classdata)
data(fbi)
fbi %>%
mutate(State = factor(State),
Rate_100k = Count/Population*100000) %>%
filter(Type == "Aggravated.assault") %>%
arrange(Year, State, Type) %>%
plot_ly(
x = ~State,
y = ~Rate_100k,
color = ~Type,
frame = ~Year,
type = "scatter",
mode = "markers"
) You can even animate by something other than time, if you’re so inclined…
There are other types of animations as well, including the ability to change plot formats, trace types, and more.
11.2 Shiny
11.3 General References
- R graph gallery interactive charts points you in the right direction for which package to use for different tasks.
There are obviously exceptions - we can figure out the exact date and approximate time that there was an earthquake along the Cascadia subduction zone based on a combination of oral histories of the indigenous people and records of a massive tsunami in Japan Excellent read, if you’re interested, and the Nature paper.↩︎